spatialDLPFC

Welcome to the spatialDLPFC project! This project involves 3 data types as well as several interactive websites, all of which you are publicly accessible for you to browse and download.

In this project we studied spatially resolved and single nucleus transcriptomics data from the dorsolateral prefrontal cortex (DLPFC) from postmortem human brain samples. From 10 neurotypical controls we generated spatially-resolved transcriptomics data using using 10x Genomics Visium across the anterior, middle, and posterior DLPFC (n = 30). We also generated single nucleus RNA-seq (snRNA-seq) data using 10x Genomics Chromium from 19 of these tissue blocks. We further generated data from 4 adjacent tissue slices with 10x Genomics Visium Spatial Proteogenomics (SPG), that can be used to benchmark spot deconvolution algorithms. This work is being was performed by the Keri Martinowich, Leonardo Collado-Torres, and Kristen Maynard teams at the Lieber Institute for Brain Development as well as Stephanie Hicks’s group from JHBSPH’s Biostatistics Department.

This project involves the GitHub repositories LieberInstitute/spatialDLPFC and LieberInstitute/DLPFC_snRNAseq.

If you tweet about this website, the data or the R package please use the #spatialLIBD hashtag. You can find previous tweets that way as shown here.

Thank you for your interest in our work!

Study Design

Study design to generate paired single-nucleus RNA sequencing (snRNA-seq) and spatially-resolved transcriptomic data across DLPFC. A. Tissue blocks were dissected across the rostral-caudal axis from 10 adult neurotypical control postmortem human brains of the DLPFC, including anterior (Ant), middle (Mid, and posterior (Post) positions (n=3 blocks per donor, n=30 blocks total). B. The same tissue blocks were used for snRNA-seq (10x Genomics 3’ gene expression assay, n=1-2 blocks per donor, n=19 samples) and spatial transcriptomics (10x Genomics Visium spatial gene expression assay, n=3 blocks per donor, n=30 samples). C. Tissue block orientation and morphology was confirmed by single molecule fluorescent in situ hybridization (smFISH) for laminar marker genes with RNAscope (SLC17A7 marking excitatory neurons in pink, MBP marking white matter in green, RELN marking layer 1 in yellow, and NR4A2 marking layer 6 in orange) and hematoxylin and eosin (H&E) staining. Spotplots depicting log transformed normalized expression (logcounts) of SNAP25, MBP, and PCP4 in the Visium data confirm the presence of gray matter, white matter, and cortical layers, respectively.

Citing our work

Please cite this manuscript if you use data from this project. Below is the citation in BibTeX format.

TODO

Below is the citation output from using citation('spatialLIBD') in R. Please run this yourself to check for any updates on how to cite spatialLIBD.

print(citation("spatialLIBD"), bibtex = TRUE)
#> 
#> To cite package 'spatialLIBD' in publications use:
#> 
#>   Pardo B, Spangler A, Weber LM, Hicks SC, Jaffe AE, Martinowich K,
#>   Maynard KR, Collado-Torres L (2022). "spatialLIBD: an R/Bioconductor
#>   package to visualize spatially-resolved transcriptomics data." _BMC
#>   Genomics_. doi:10.1186/s12864-022-08601-w
#>   <https://doi.org/10.1186/s12864-022-08601-w>,
#>   <https://doi.org/10.1186/s12864-022-08601-w>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Article{,
#>     title = {spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data},
#>     author = {Brenda Pardo and Abby Spangler and Lukas M. Weber and Stephanie C. Hicks and Andrew E. Jaffe and Keri Martinowich and Kristen R. Maynard and Leonardo Collado-Torres},
#>     year = {2022},
#>     journal = {BMC Genomics},
#>     doi = {10.1186/s12864-022-08601-w},
#>     url = {https://doi.org/10.1186/s12864-022-08601-w},
#>   }
#> 
#>   Maynard KR, Collado-Torres L, Weber LM, Uytingco C, Barry BK,
#>   Williams SR, II JLC, Tran MN, Besich Z, Tippani M, Chew J, Yin Y,
#>   Kleinman JE, Hyde TM, Rao N, Hicks SC, Martinowich K, Jaffe AE
#>   (2021). "Transcriptome-scale spatial gene expression in the human
#>   dorsolateral prefrontal cortex." _Nature Neuroscience_.
#>   doi:10.1038/s41593-020-00787-0
#>   <https://doi.org/10.1038/s41593-020-00787-0>,
#>   <https://www.nature.com/articles/s41593-020-00787-0>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Article{,
#>     title = {Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex},
#>     author = {Kristen R. Maynard and Leonardo Collado-Torres and Lukas M. Weber and Cedric Uytingco and Brianna K. Barry and Stephen R. Williams and Joseph L. Catallini II and Matthew N. Tran and Zachary Besich and Madhavi Tippani and Jennifer Chew and Yifeng Yin and Joel E. Kleinman and Thomas M. Hyde and Nikhil Rao and Stephanie C. Hicks and Keri Martinowich and Andrew E. Jaffe},
#>     year = {2021},
#>     journal = {Nature Neuroscience},
#>     doi = {10.1038/s41593-020-00787-0},
#>     url = {https://www.nature.com/articles/s41593-020-00787-0},
#>   }

Please note that the spatialLIBD was only made possible thanks to many other R and bioinformatics software authors, which are cited either in the vignettes and/or the paper(s) describing the package.

Interactive Websites

We provide the following interactive websites:

If you are interested in running the spatialLIBD applications locally, you can do so thanks to the spatialLIBD::run_app(), which you can also use with your own data as shown in our vignette for publicly available datasets provided by 10x Genomics.

## Run this web application locally
spatialLIBD::run_app()
## You will have more control about the length of the
## session and memory usage.
## You could also use this function to visualize your
## own data given some requirements described
## in detail in the package vignette documentation
## at http://research.libd.org/spatialLIBD/.

All of these websites are powered by open source software, namely:

Data Access

We highly value open data sharing and believe that doing so accelerates science, as was the case between our HumanPilot and BayesSpace projects, documented on this slide. We also value public questions, as they allow other users to learn from the answers. If you have any questions, please ask them on a public forum such as LieberInstitute/spatialDLPFC/issues.

Processed Data

spatialLIBD also allows you to access the data from this project. That is, a:

You can use the zellkonverter Bioconductor package to convert any of them into Python AnnData objects. If you browse our code, you can find examples of such conversions.

If you are unfamiliar with these tools, you might want to check the LIBD rstats club videos and resources.

Installing spatialLIBD

Get the latest stable R release from CRAN. Then install spatialLIBD from Bioconductor with the following code:

## Install BiocManager in order to install Bioconductor packages properly
if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}

## Check that you have a valid R/Bioconductor installation
BiocManager::valid()

## Now install spatialLIBD from Bioconductor
## (this version has been tested on macOS, winOS, linux)
BiocManager::install("spatialLIBD")

## If you need the development version from GitHub you can use the following:
# BiocManager::install("LieberInstitute/spatialLIBD")
## Note that this version might include changes that have not been tested
## properly on all operating systems.
Access the data

Through the spatialLIBD package you can access the processed data in it’s final R format.

Processed data

Using spatialLIBD you can access the Human DLPFC spatial transcriptomics data from the 10x Genomics Visium platform. For example, this is the code you can use to access the spatially-resolved data. For more details, check the help file for fetch_data().

## Check that you have a recent version of spatialLIBD installed
stopifnot(packageVersion("spatialLIBD") >= "1.11.2")

## Download the spot-level data
spe <- spatialLIBD::fetch_data(type = "spatialDLPFC_Visium")

## This is a SpatialExperiment object
spe
#> class: SpatialExperiment
#> dim: 28916 113927
#> metadata(1): BayesSpace.data
#> assays(2): counts logcounts
#> rownames(28916): ENSG00000243485 ENSG00000238009 ... ENSG00000278817 ENSG00000277196
#> rowData names(7): source type ... gene_type gene_search
#> colnames(113927): AAACAACGAATAGTTC-1 AAACAAGTATCTCCCA-1 ... TTGTTTGTATTACACG-1
#>   TTGTTTGTGTAAATTC-1
#> colData names(93): age array_col ... VistoSeg_count VistoSeg_percent
#> reducedDimNames(8): 10x_pca 10x_tsne ... HARMONY UMAP.HARMONY
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
#> imgData names(4): sample_id image_id data scaleFactor

## Note the memory size
lobstr::object_size(spe)
#> 6.96 GB

## Set the cluster colors
colors_BayesSpace <- Polychrome::palette36.colors(28)
names(colors_BayesSpace) <- seq_len(28)

## Remake the logo image with histology information
p09 <- spatialLIBD::vis_clus(
    spe = spe,
    clustervar = "BayesSpace_harmony_09",
    sampleid = "Br6522_ant",
    colors = colors_BayesSpace,
    ... = " spatialDLPFC Human Brain - Sp09 domains\nMade with github.com/LieberInstitute/spatialDLPFC + spatiaLIBD"
)
p09

## Repeat but for Sp16
p16 <- spatialLIBD::vis_clus(
    spe = spe,
    clustervar = "BayesSpace_harmony_16",
    sampleid = "Br6522_ant",
    colors = colors_BayesSpace,
    ... = " spatialDLPFC Human Brain - Sp16 domains\nMade with github.com/LieberInstitute/spatialDLPFC + spatiaLIBD"
)
p16

Raw data

You can access all the raw data through Globus (jhpce#spatialDLPFC and jhpce#DLPFC_snRNAseq). This includes all the input FASTQ files as well as the outputs from tools such as SpaceRanger or CellRanger. The files are mostly organized following the LieberInstitute/template_project project structure.

Internal

  • JHPCE locations:
    • /dcs04/lieber/lcolladotor/spatialDLPFC_LIBD4035/spatialDLPFC
    • /dcs04/lieber/lcolladotor/deconvolution_LIBD4030/DLPFC_snRNAseq
  • Slack channel: libd_dlpfc_spatial.

Files: spatialDLPFC

  • code: R, python, and shell scripts for running various analyses.
    • spot_deconvo: cell-type deconvolution within Visium spots, enabled by tools like tangram, cell2location, and cellpose
    • spython: older legacy testing scripts mostly replaced by spot_deconvo
  • plots: plots generated by RMarkdown or R analysis scripts in .pdf or .png format
  • processed-data
    • images_spatialLIBD: images used for running SpaceRanger
    • NextSeq: SpaceRanger output files
    • rdata: R objects
  • raw-data
    • FASTQ: FASTQ files from NextSeq runs.
    • FASTQ_renamed: renamed symbolic links to the original FASTQs, with consistent nomenclature
    • Images: raw images from the scanner in .tif format and around 3 GB per sample.
    • images_raw_align_json
    • psychENCODE: external data from PsychENCODE, originally retrieved from here
    • sample_info: spreadsheet with information about samples (sample ID, sample name, slide serial number, capture area ID)

This project is organized along the guidelines at https://lcolladotor.github.io/bioc_team_ds/organizing-your-work.html#.Yaf9fPHMIdk.

Files: DLPFC_snRNAseq

TODO